arXivDaily arXiv每日学术速递 周一至周五更新
2606.20145 2026-06-19 q-fin.ST cond-mat.stat-mech physics.data-an q-fin.MF q-fin.RM 交叉投稿

Trends, Volatility, Correlations, and Critical Phenomena in Financial Markets

金融市场中的趋势、波动率、相关性和临界现象

Sara A. Safari, Christoph Schmidhuber

AI总结 基于当前市场趋势预测未来波动率和相关性,发现趋势强度与波动率、相关性呈二次关系,改进风险预测并支持临界点晶格气体模型。

Comments 31 pages, 9 figures

详情
AI中文摘要

我们基于金融市场的当前趋势预测未来的波动率和相关性。这补充了先前的工作,该工作通过当前趋势强度的三次多项式来建模未来预期收益。经验上,我们观察到在强烈上升或下降趋势期间,波动率和相关性往往逐日增加。这种效应在下降趋势中尤为显著。它可以通过当前趋势强度的二次多项式精确量化,这细化了波动率和相关性的常见均值回归模型。我们的结果通过考虑市场趋势改进了市场风险的预测。它们也支持最近一项将金融市场建模为接近其临界点的晶格气体的提议。

英文摘要

We forecast future volatilities and correlations of financial markets based on the current trends in these markets. This complements previous work that models future expected returns by a cubic polynomial of the current trend strength. Empirically, we observe that volatilities and correlations tend to increase day after day in times of strong up- or down-trends. This effect is particularly pronounced in down-trends. It can be accurately quantified by quadratic polynomials of today's trend strengths, which refine common mean-reversion models of volatilities and correlations. Our results improve the prediction of market risk by accounting for market trends. They also support a recent proposal to model financial markets by a lattice gas near its critical point.

2503.13328 2026-06-19 q-fin.MF math.PR 版本更新

Model-independent upper bounds for the prices of Bermudan options with convex payoffs

凸收益百慕大期权价格的无模型上界

David Hobson, Dominykas Norgilas

AI总结 研究在给定欧式期权价格下,寻找具有凸收益的百慕大期权价格的无套利上界,通过刻画对偶问题并假设测度满足分散性条件完全求解,发现标准设定不足以定义最优模型,需要额外随机化。

Comments 55 pages, 6 figures. In the new version we work with arbitrary convex payoffs and marginal distributions that satisfy the Dispersion Assumption

详情
AI中文摘要

假设 $\mu$ 和 $\nu$ 是 $\mathbb{R}$ 上的概率测度,满足 $\mu \leq_{cx} \nu$。设 $a$ 和 $b$ 是 $\mathbb{R}$ 上的凸函数,且 $a \geq b \geq 0$。我们感兴趣的是寻找 $$\sup_{\mathbf{M}} \sup_{\tau} \mathbb{E}^{\mathbf{M}} \left[ a(X) I_{ \{ \tau = 1 \} } + b(Y) I_{ \{ \tau = 2 \} } \right] $$ 其中第一个上确界取遍所有一致模型 $\mathbf{M}$(即过滤概率空间 $(\Omega, \mathbf{F}, \mathbb{F}, \mathbb{P})$,使得 $Z=(z,Z_1,Z_2)=(\int_{\mathbb{R}} x \mu(dx) = \int_{\mathbb{R}} y \nu(dy), X, Y)$ 是一个 $(\mathbb{F},\mathbb{P})$ 鞅,且在 $\mathbb{P}$ 下 $X$ 服从分布 $\mu$,$Y$ 服从分布 $\nu$),第二个上确界中的 $\tau$ 是取值于 $\{1,2\}$ 的 $(\mathbb{F},\mathbb{P})$ 停时。我们的贡献首先是刻画并简化对偶问题,其次是在对测度 $\mu$ 和 $\nu$ 的一些结构假设(即 $\mu$ 和 $\nu$ 是绝对连续的概率测度且满足分散性假设)下完全求解该问题。一个关键发现是,由 $Z$ 生成的过滤的标准设定不足以定义最优模型,需要额外的随机化。即使边际分布 $\mu$ 和 $\nu$ 是无原子的,这一结论仍然成立。该问题可解释为:在给定同时到期的欧式期权价格的情况下,寻找具有两个可能行权日的百慕大期权价格的稳健或无模型无套利上界。

英文摘要

Suppose $μ$ and $ν$ are probability measures on $\mathbb{R}$ satisfying $μ\leq_{cx} ν$. Let $a$ and $b$ be convex functions on $\mathbb{R}$ with $a \geq b \geq 0$. We are interested in finding $$\sup_{\mathbf{M}} \sup_τ \mathbb{E}^{\mathbf{M}} \left[ a(X) I_{ \{ τ= 1 \} } + b(Y) I_{ \{ τ= 2 \} } \right] $$ where the first supremum is taken over consistent models $\mathbf{M}$ (i.e., filtered probability spaces $(Ω, \mathbf{F}, \mathbb{F}, \mathbb{P})$ such that $Z=(z,Z_1,Z_2)=(\int_{\mathbb{R}} x μ(dx) = \int_{\mathbb{R}} y ν(dy), X, Y)$ is a $(\mathbb{F},\mathbb{P})$ martingale, where $X$ has law $μ$ and $Y$ has law $ν$ under $\mathbb{P}$) and $τ$ in the second supremum is a $(\mathbb{F},\mathbb{P})$-stopping time taking values in $\{1,2\}$. Our contributions are first to characterise and simplify the dual problem, and second to completely solve the problem under some structural assumptions on the measures $μ$ and $ν$ (namely that $μ$ and $ν$ are absolutely continuous probability measures that satisfy the Dispersion Assumption). A key finding is that the canonical set-up in which the filtration is that generated by $Z$ is not rich enough to define an optimal model and additional randomisation is required. This holds even though the marginal laws $μ$ and $ν$ are atom-free. The problem has an interpretation of finding the robust, or model-free, no-arbitrage bound on the price of a Bermudan option with two possible exercise dates, given the prices of co-maturing European options.